Autocorrelation Function and Power Spectral Density

When plotting graphs in MATLAB, various challenges may arise. For instance, when plotting autocorrelation functions, careful consideration must be given to selecting appropriate window functions and determining optimal sliding window sizes. The autocorr() function from the Signal Processing Toolbox can be utilized with parameters like 'NumLags' to control the correlation range and 'NumSTD' for confidence intervals. For power spectral density plots, critical factors include signal sampling rates and phase delays, which can be addressed using functions like pwelch() for Welch's method or periodogram() for basic spectral estimation. To effectively resolve these plotting issues, MATLAB offers specialized toolboxes such as the Signal Processing Toolbox and Control System Toolbox. Parameter adjustments through functions like window() for applying Hamming, Hanning, or Kaiser windows, along with strategic function selection, significantly enhance graph quality and analytical accuracy. Therefore, when conducting data analysis and visualization in MATLAB, thorough consideration of data characteristics and analysis objectives is essential for selecting optimal plotting methodologies and tools. Implementation examples include using xcorr() for cross-correlation analysis and fft() for Fourier transform-based spectral computations.